JHEP03(2014)032
Published for SISSA by SpringerReceived: December 19, 2013 Accepted: February 7, 2014 Published: March 5, 2014
Study of double parton scattering using W + 2-jet
events in proton-proton collisions at
√
s = 7 TeV
The CMS collaboration
E-mail: cms-publication-committee-chair@cern.ch
Abstract: Double parton scattering is investigated in proton-proton collisions at √s =
7 TeV where the final state includes a W boson, which decays into a muon and a neutrino,
and two jets. The data sample corresponds to an integrated luminosity of 5 fb−1, collected
with the CMS detector at the LHC. Observables sensitive to double parton scattering are investigated after being corrected for detector effects and selection efficiencies. The fraction of W + 2-jet events due to double parton scattering is measured to be 0.055 ±
0.002 (stat.) ± 0.014 (syst.). The effective cross section, σeff, characterizing the effective
transverse area of hard partonic interactions in collisions between protons is measured to be 20.7 ± 0.8 (stat.) ± 6.6 (syst.) mb.
Keywords: Jet physics, Hadron-Hadron Scattering, QCD
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Contents1 Introduction 1
2 Effective cross section 2
3 Experimental methods 5
3.1 Simulated samples 5
3.2 Event selection 7
4 Unfolding and comparison with simulations 9
5 Strategy for the extraction of the DPS fraction 13
5.1 DPS signal template 13
5.2 Background template 14
6 The DPS fraction in data 16
7 Determination of the effective cross section 18
7.1 Measurement of R 19
7.2 Measurement of σ2j0 19
8 Summary 21
The CMS collaboration 27
1 Introduction
In high-energy proton-proton (pp) collisions at the Large Hadron Collider (LHC),
semi-hard parton-parton scattering, producing particles with transverse momenta pT of a few
GeV, dominates the inelastic cross section. In such processes longitudinal momentum
fractions, given by x ∼ 2pT/
√
s, of values down to O(10−3) are probed. At these values of
x, the parton densities are large causing a sizable probability for two or more parton-parton
scatterings within the same pp interaction [1]. Such multi-parton interactions (MPI) at
semi-hard scales of a few GeVs have been observed in high-energy hadronic collisions [2].
Conversely, the evidence for hard double parton scattering (DPS) processes in the same pp collision at scales of a few tens of GeV is still relatively weak. In processes where a W
and two jets are produced, the x values are larger, x ∼ 10−2, and the parton densities are
lower. However, a sizable contribution to DPS can still be expected if the second scattering, yielding two jets, occurs at a high rate. The study of DPS processes is important because it
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q q l(l) ) ν ( ν +(-) W g g g g g q g g +(-) W (l) l ) ν ( ν qFigure 1. Feynman diagrams for W + 2-jet production from (left) double parton scattering and (right) single parton scattering.
on the multi-parton correlations in the hadronic wave function [4]. DPS also constitutes a
background to new physics searches at the LHC [5–7].
Various measurements in pp and pp collisions at √s = 63 GeV [8], 630 GeV [9], and
1.8 TeV [10] are consistent with DPS contributions to multijet final states, as well as to γ +
3-jet events at√s = 1.8 TeV [11] and 1.96 TeV [12]. Additional searches for DPS have been
proposed via double Drell-Yan, four jet, and same-sign WW production, as well as in W
production associated with jets [13–20]. This paper presents a study of DPS based on W
+ 2-jet events in pp collisions at 7 TeV. DPS with a W + 2-jet final state occurs when one hard interaction produces a W boson and another produces a dijet in the same pp collision,
as sketched in figure1(left). The W + 2-jet process is attractive because the muonic decay
of the W provides a clean tag and the large dijet production cross section increases the probability of observing DPS. Events containing a W + 2-jet final state originating from
single parton scattering (SPS) constitute an irreducible background (figure 1(right)). The
ATLAS collaboration has carried out a similar DPS measurement using W + 2-jet events
at√s = 7 TeV [21].
The outline of this paper is as follows. Section 2 describes DPS in terms of effective cross section and defines the relevant observables. Section 3 presents a brief description of the Compact Muon Solenoid (CMS) detector, the data and the simulated samples, as well as the event selection criteria. Section 4 summarizes the unfolding of the DPS-sensitive observables, the systematic effects, and the comparison of data and simulation. The method to extract the DPS fraction is discussed in section 5. Section 6 presents the extraction of the DPS fraction from the data and corresponding systematic uncertainties. The measurement of the effective cross section is described in section 7.
2 Effective cross section
The effective cross section, σeff, is a measure of the transverse distribution of partons inside
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individual processes. If A and B are two independent processes, whose production cross
sections are σAand σB, respectively, σeff can be written as:
σeff= m 2 σA· σB σDPS A+B , (2.1)
where “m” is a symmetry factor for indistinguishable (m = 1) and distinguishable (m = 2)
final-states and σA+BDPS is the cross section of the two processes to occur simultaneously.
According to various phenomenological studies [22–24], the above cross sections should
be inclusive. This requirement makes the determination of σeff independent of the
spe-cific mechanisms of the first and second interactions, as well as of the parton distribution
functions (PDF). Inclusive σA+BDPS also includes contributions from higher number of
par-ton scatters.
However, in the present analysis an exclusive selection is performed by considering the
events with one W boson and exactly two jets with pT > 20 GeV/c and pseudorapidity, η,
within ±2. The pseudorapidity is defined as η = − ln[tan(θ/2)], where θ is the polar angle measured with respect to the anti-clockwise beam direction. This sample should have a significant contribution from events where one interaction produces only a W boson and
no jet with pT > 20 GeV/c within |η| < 2.0 and the other interaction produces exactly two
jets with pT > 20 GeV/c and |η| < 2.0.
Experimentally, the exclusive selection is necessary to identify the jets from the second interaction. In this case, DPS-sensitive observables can be defined based on the
back-to-back topology of the two jets. From a sample of simulated events generated with
MadGraph 5 [25, 26] followed by hadronization and parton showering (PS) using the
4C tune [27] of pythia 8 [28], it is found that σeff changes by only 2–3% if an inclusive
selection is applied.
In order to account for missing contributions of a larger number of parton scatterings,
corrections [22–24] were proposed to a previous DPS measurement of CDF [11]. However,
in the kinematic region of the present study, due to the requirement of having exactly 2 jets, the contribution of triple and higher number of scatters is expected to be small and is estimated, with the same sample of simulated events as mentioned above, to be less than 1% of the DPS contribution. Therefore, for the present analysis no additional correction is required for the exclusive selection.
Assuming independent interactions from DPS, σeff can be rewritten in terms of the
cross sections at the stable particle level (defined as lifetime, cτ > 10 mm) within the
detector acceptance. For the case of the W + 2-jet process, σeff becomes:
σeff=
σW+0j0
σW+2j0DPS · σ
0
2j, (2.2)
where the prime indicates that the cross sections are obtained at particle level. The σW+0j0
and σ2j0 are the particle-level cross sections for W-boson production associated with zero-jet
and for dijet events, respectively. The particle-level cross section for DPS events producing a W + 0-jet from the first interaction and exactly two jets from the second interaction is
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sample; therefore σeff can be reformulated in terms of the yield of W bosons associated
with zero jets and the yield associated with DPS:
σeff=
NW+0j0
NW+2j0DPS · σ
0
2j. (2.3)
If we define the DPS fraction as
fDPS =
NW+2j0DPS
NW+2j0 , (2.4)
σeff can be written as
σeff= NW+0j0 fDPS· NW+2j0 · σ2j0 , (2.5) or σeff= R fDPS · σ02j, (2.6)
where R = NW+0j0 /NW+2j0 . Thus, the determination of the effective cross section reduces
to a measurement of R, σ2j0 , and fDPS.
For the extraction of the DPS fraction, fDPS, observables that can discriminate between
SPS and DPS are needed. For DPS events, the W and the dijet system are independent of each other, while for SPS events they are highly correlated. It is thus possible to define several observables that discriminate between DPS and SPS events. The present analysis uses the following observables, which were also considered in previous DPS measurements at the LHC and the Tevatron:
• the relative pT-balance between the two jets, ∆rel pT, defined as:
∆rel pT=
|~pT(j1) + ~pT(j2)|
|~pT(j1)| + |~pT(j2)|
. (2.7)
Here ~pT(j1) and ~pT(j2) are the transverse momentum vectors of the leading (in pT)
and subleading jets. In DPS events, at leading order (LO), the two jets balance each
other and ∆rel pT is small, which is not the case for SPS events.
• The azimuthal angle between the W-boson and the dijet system, ∆S, defined as: ∆S = arccos ~ pT(µ, E/ ) · ~T pT(j1, j2) |~pT(µ, E/ )| · |~T pT(j1, j2)| , (2.8)
where ~pT(µ, E/ ) and ~T pT(j1, j2) are the combined transverse momentum vectors of
(µ, E/ ) and the two jets, respectively, with ET / as the missing transverse energy inT
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3 Experimental methodsIn the present analysis a sample of W + 2-jet events is selected from a data sample of
pp collisions at √s = 7 TeV with the CMS detector. The data sample corresponds to an
integrated luminosity of 5 fb−1. The distributions are fully corrected for detector effects
and efficiencies. These distributions are used for the extraction of the DPS fraction and the determination of the effective cross section. The dijet production cross section required for the determination of the effective cross section is measured with a pp data sample
collected in 2010 also at√s = 7 TeV. This sample corresponds to an integrated luminosity
of 35 pb−1.
The central feature of the CMS apparatus is a superconducting solenoid of 6 m internal diameter, providing a magnetic field of 3.8 T. Within the superconducting solenoid volume are a silicon pixel and strip tracker, a lead tungstate crystal electromagnetic calorimeter (ECAL), and a brass/scintillator hadron calorimeter (HCAL). Muons are measured in gas-ionization detectors embedded in the steel flux return yoke outside the solenoid. In
addition, CMS has extensive forward calorimetry. The CMS experiment uses a
right-handed coordinate system, with the origin at the nominal interaction point, the x axis pointing to the centre of the LHC ring, the y axis pointing up (perpendicular to the plane of the LHC ring), and the z axis along the anticlockwise-beam direction. The polar angle θ is measured from the positive z axis and the azimuthal angle φ is measured in the x-y
plane. A more detailed description of the CMS apparatus can be found in ref. [29].
3.1 Simulated samples
Samples of W + jets events are generated with MadGraph 5 followed by hadronization and
parton showering using the Z2 tune [30] of pythia 6 (version 6.4.25) [31]. The MadGraph
event generator produces parton-level events with a W boson and up to four partons in the final state on the basis of matrix element (ME) calculations. The ME/PS matching scale µ is taken to be 20 GeV, and the factorization and renormalization scales are set to
q2 = MW2 c2+ (pTW)2, where MW and pWT are the mass and transverse momentum of the W
boson, respectively.
Samples of Z/γ* + jets and tt events are also simulated with MadGraph 5.
Single-top-quark samples are generated with powheg 2 [32]. Samples of WW and WZ events
are generated with the pythia 6 Monte Carlo (MC) event generator. Contributions of multijet events from QCD interactions are estimated from data, as discussed later. The inclusive cross sections for simulated processes are normalized to the next-to-leading-order (NLO), next-to-NLO (NNLO), or next-to-next-to-leading-log (NNLL) order calculations.
Table1 gives the values of the cross sections of the simulated processes and their
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Process Cross section (pb)
W → µν 10500 ± 5% (NNLO) [33]
W → τ ν 10500 ± 5% (NNLO) [33]
tt 160 ± 7% (NNLL) [34]
Single top quark 85 ± 5% (NLO) [35–37]
Drell-Yan 3050 ± 4.3% (NNLO) [33]
Diboson (WW + WZ) 61 ± 10% (NLO) [38]
Multijet Estimated from data by fitting
E/ distribution in control regionT
Table 1. Cross sections of the various processes and their uncertainties.
The simulated samples are processed and reconstructed in the same manner as the
col-lision data. The detector response is simulated in detail by using the Geant4 package [39].
The samples include additional interactions per beam crossing (the so-called pileup), which match the corresponding distribution in data.
In addition to these fully simulated samples, various simulations at particle level are compared with the fully corrected DPS-sensitive observables.
• MadGraph 5 + pythia 8: W + jets events are generated by means of MadGraph 5 (as discussed before) followed by hadronization and parton showering using the
4C tune of pythia 8. The MPI [1] are simulated with the pythia 8 event
genera-tor. In order to see the effects of MPI, events are also produced without the MPI contribution by pythia 8. For the systematic studies, hadronization and parton
showering of MadGraph 5 events are performed with pythia 6 tune Z2* [40], with
and without MPI.
• powheg 2 + pythia 6 (herwig 6): W + 2-jet events are also produced up to NLO accuracy with the powheg 2 event generator with the “Multi-scale improved NLO”
(MiNLO) method [41]. The W + 2-jet samples simulated with the powheg 2 +
MiNLO describe satisfactorily the inclusive W + jet production data as well [42].
Hadronization and parton showering is carried out with pythia 6, tune Z2*. To
assess the effect of angular-ordered showering, herwig 6 (version 6.520) [43, 44] is
also used for the parton showering.
• pythia 8: W + jets events are generated with the 4C tune of the pythia 8 event gen-erator, which produces hard subprocesses with a W boson and either zero or one addi-tional parton in the final state. It also performs hadronization and parton showering. The MPI model is similar in pythia 6 and pythia 8, with the free parameters tuned to the underlying event data obtained at the LHC. The key features of the model are:
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of MPI. A factor with a free parameter, pT0, is introduced to regularize an otherwise
divergent partonic cross section,
αs2(p2T+ pT02) α2 s(p2T) · p 4 T (p2T+ pT02)2 , (3.1) with pT0( √ s) = pT0( √ s0) √s √ s0 . (3.2)
Here√s0 = 1.8 TeV and is a parameter characterizing the energy dependence of pT0.
• A Poisson distribution for the number of MPI in an event, with a mean that depends on the overlap of the matter distribution of the hadrons in impact parameter space.
The impact parameter profile gives a measure of σeff. The present model uses the
convolution of the matter distributions of the two incoming hadrons as an estimate
of the impact parameter profile. The overlap function is of the form e−bZ, where b is
the impact parameter and Z is a free parameter.
The MPI model used here [31] includes parton showers for the MPI processes as well as
MPI processes interleaved with initial state radiation.
Events simulated with LO event generators, i.e. MadGraph 5, pythia 6, and
pythia 8, use the CTEQ6L [45] PDF set, whereas in the NLO event generation with
powheg 2 the CTEQ6M [45] PDF set is used.
3.2 Event selection
Events were selected online when at least one muon candidate was found. A muon candidate consists of a track with hits in the muon system and a transverse momentum greater than a threshold. The threshold was increased with increasing instantaneous luminosity in order to keep the rate within the allocated trigger bandwidth for muon triggers. The offline selection requires exactly one muon reconstructed in the muon detector and the silicon tracker. Muon candidates are required to satisfy identification criteria based on the number of hits in the muon detector and the tracker, their transverse impact parameter with respect to the beam
axis, and the goodness of the global fit χ2/(number of degree of freedom) [46] for the tracks
in the tracking system and the muon chambers. The background from jets misidentified as muons and from semileptonic decays of heavy quarks is suppressed by applying an isolation condition on the muon candidates. The muon candidate is considered to be isolated if the
isolation variable [47], I, is smaller than 0.1.
The selected muon is required to have pT > 35 GeV/c and |η| < 2.1. The trigger
efficiency for the selected muon is larger than 90% and the muon selection efficiency is
about 95% [46]. The muon candidate is retained only if associated with the primary vertex
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Jets and E/ are reconstructed with the particle-flow (PF) algorithm [T 48], which
com-bines information from several sub-detectors. The jet reconstruction is based on the anti-kT
clustering algorithm [49–51] with a distance parameter of 0.5. Jets are required to have
pT > 20 GeV/c and |η| < 2.0 to ensure that they are well reconstructed and fall within
the tracker acceptance. Jets are required to satisfy identification criteria that eliminate jet
candidates originating from noisy channels in the hadron calorimeter [52]. Jet energy scale
(JES) corrections are applied to account for the non-linear response of the calorimeters to the particle energy and other instrumental effects. These corrections are based on in-situ
measurements using dijet, γ + jet, and Z + jet data samples [53]. Pileup and the
under-lying event can contribute additional energy to the reconstructed jets. The median energy density due to pileup is evaluated in each event and the corresponding energy is subtracted
from each jet [54]. Jets are rejected if they overlap with selected muons within a cone of
radius 0.5. In order to reject additional jets from pileup interactions, a pileup mitigating variable β is utilized, defined as:
β = Σ psignal vertexT Σ pall T , (3.3)
where Σ(psignal vertexT ) is the sum of the pT of all charged constituents in a jet associated
with the signal vertex and Σ(pall
T) is the sum of the pT of all charged constituents in a jet.
Jets are required to have β > 0.4 .
The W transverse mass (MT) is defined as:
MT =
r
2 · pµT· E/ ·T 1 − cos ∆φ[µ, E/ ]T
. (3.4)
The E/T is defined as the negative vector sum of the transverse momenta of all
re-constructed particle candidates in the event, E/T = −Σi~pT(i). The reconstructed E/T is
corrected for the non-compensating nature of the calorimeters and detector misalignment
using the procedure described in ref. [55]. This procedure uses all corrected jets which have
pT > 10 GeV/c and less than 90% of their energy in the ECAL. The E/ is also corrected forT
the effect of the azimuthal variation of the tracker acceptance and the calorimeter align-ment. The correction factor is calculated as a function of the number of reconstructed
vertices and also as a function of ΣET, where ΣET is the total transverse energy measured
in the calorimeter. The angle ∆φ[µ, E/ ] is measured between the muon µ and the ET /T
direction in the azimuthal plane.
Events are required to have exactly one muon with pT > 35 GeV/c, |η| < 2.1, and
E/T > 30 GeV. The transverse mass is required to be greater than 50 GeV/c2. Selected
events are required to have exactly two jets with pT> 20 GeV/c and |η| < 2.0. The criteria
used in the selection are summarized in table2.
The kinematic distributions of the jets in the selected events are reproduced by the
MC simulations as shown in figure 2. Figure 3 shows the comparison of data with MC
simulations for ∆relpT (left) and ∆S (right) at the detector level. Data and MC simulation
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W → µν selection Jet selection
Single-muon trigger Anti-kT PF jet with R = 0.5
Muon ID and isolation pT > 20 GeV/c, |η| < 2.0
Exactly one muon pT> 35 GeV/c, |η| < 2.1 β > 0.4
E/ > 30 GeV/cT ∆Rj,µ> 0.5
W-boson transverse mass > 50 GeV/c2 exactly two jets
Table 2. Summary of the W + 2-jet event selection and reconstruction criteria at the detector level.
Process Number of events
W → µν (2.3 ± 0.12) × 105 W → τ ν (3.7 ± 0.20) × 103 Top quark (9.4 ± 0.69) × 103 Drell-Yan (5.3 ± 0.26) × 103 Diboson (2.6 ± 0.26) × 103 Multijet (1.1 ± 0.34) × 103
Total expected events (2.5 ± 0.14) × 105
Data (2.4 ± 0.0049) × 105
Table 3. Expected yields for various processes for 5 fb−1and observed number of events in the data. The top production background is the sum of the single-top-quark and tt processes. The estimated event yields from the simulated samples include uncertainties in the respective cross sections.
a concern for the present analysis because the contribution of events having large pT jet
is very small, e.g., only 3% of the W + 2-jet events have a jet pT larger than 100 GeV/c,
where the description starts to deviate from the data. There is a small level of background contamination in the selected W + 2-jet samples. The dominant background contribution comes from top-quark production (single top-quark and pair production) and Drell-Yan processes. The contribution of the multijet background is less than 0.5%. This contribution is estimated by defining a control region with the requirement of a non-isolated muon, with
I > 0.1. A template of the E/ distribution for the multijet background is constructed byT
using events in this control region. This template is then used to estimate the contribution
of the multijet background by fitting the E/ distribution in the signal region, with I < 0.1.T
Table3 summarizes the expected number of events for the processes listed in table 1, for
an integrated luminosity of 5 fb−1. The total contribution from the background events is
about 10% but the effect on the shape of the DPS-sensitive observables is less than 1%.
4 Unfolding and comparison with simulations
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j N 0 1 2 3 4 5 6 7 Data/MC 0.8 0.91 1.1 1.2 1.3 Uncertainty jN
0 1 2 3 4 5 6 7
) j d(N evt dN 3 10 4 10 5 10 6 10 7 10 810 DataW →µν, MADGRAPH5 + PYTHIA6
Background -1 Ldt = 5 fb
∫
= 7 TeV s CMS [GeV/c] (j1) T p 20 40 60 80 100 120 140 160 180 200 Data/MC 0.8 0.91 1.1 1.2 1.3 Uncertainty[GeV/c]
(j1) Tp
20406080100
120
140
160
180
200
) (j1) T d(p evt dN 3 10 4 10 5 10 Data , MADGRAPH5 + PYTHIA6 ν µ → W Background -1 Ldt = 5 fb∫
= 7 TeV s CMS [GeV/c] (j2) T p 20 40 60 80 100 120 140 160 180 200 Data/MC 0.8 0.91 1.1 1.2 1.3 Uncertainty[GeV/c]
(j2) Tp
20406080100
120
140
160
180
200
) (j2) T d(p evt dN 2 10 3 10 4 10 5 10 6 10 Data , MADGRAPH5 + PYTHIA6 ν µ → W Background -1 Ldt = 5 fb∫
= 7 TeV s CMS [GeV/c] ) T E µ ( T p 0 20 40 60 80 100 120 140 160 180 200 Data/MC 0.8 0.91 1.1 1.2 1.3 Uncertainty[GeV/c]
) T E µ ( Tp
020406080100
120
140
160
180
200
) )T E µ ( T d(p evt dN 3 10 4 10 5 10 Data , MADGRAPH5 + PYTHIA6 ν µ → W Background -1 Ldt = 5 fb∫
= 7 TeV s CMSFigure 2. Detector-level comparison of data with MC simulations for the multiplicity (top left) of jets (Nj) with pT> 20 GeV/c and |η| < 2.0. Data and simulations for the sample with exactly two jets are plotted as a function of the pTof the leading (top right) and subleading (lower left) jets, as well as of the magnitude of the vector sum of the muon pTand E/ (lower right). The backgroundT distribution represents the sum of the contributions of Drell-Yan, W → τ ν, diboson, multijet, tt, and single-top-quark processes. The bottom panels show the ratio of the data and simulated distributions. The band shows the total uncertainty, with the contributions of the jet energy scale uncertainty and the statistical uncertainties of the MC samples added in quadrature. The error bars on the ratio histogram represent the statistical uncertainty of the data and the simulated samples added in quadrature.
The distributions of the DPS-sensitive observables for the selected events are corrected for selection efficiencies and detector effects. The trigger efficiency does not bias the shape of the DPS-sensitive observables; this was checked by comparing simulated samples with and without the trigger requirement. The selected events are mainly SPS events and the sample contains only a few percent of DPS events. However, in the DPS-sensitive region,
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T p rel ∆ 0 0.2 0.4 0.6 0.8 1 Data/MC 0.8 0.91 1.1 1.2 1.3 Uncertainty Tp
rel∆
0 0.2 0.4 0.6 0.8 1
) T p rel ∆ d( evt dN 2 10 3 10 4 10 5 10 Data , MADGRAPH5 + PYTHIA6 ν µ → W Background -1 Ldt = 5 fb∫
= 7 TeV s CMS S ∆ 0 0.5 1 1.5 2 2.5 3 Data/MC 0.8 0.91 1.1 1.2 1.3 UncertaintyS
∆
0 0.5 1 1.5 2 2.5 3
S) ∆ d( evt dN 2 10 3 10 4 10 5 10 Data , MADGRAPH5 + PYTHIA6 ν µ → W Background -1 Ldt = 5 fb∫
= 7 TeV s CMSFigure 3. Comparison of data with MC simulations at detector level for the DPS-sensitive ob-servables ∆rel pT (left), and ∆S (right). The background distribution represents the sum of the contributions of Drell-Yan, W → τ ν, diboson, multijet, tt, and single-top-quark processes. The bottom panels show the ratio of the data and simulated distributions. The band shows the total uncertainty, with the contributions of the jet energy scale uncertainty and the statistical uncertain-ties of the MC samples added in quadrature. The error bars on the ratio histogram represent the statistical uncertainty of the data and the simulated samples added in quadrature.
1 µ : pT> 35 GeV/c and |η| < 2.1
E/ > 30 GeV and MT T > 50 GeV/c2
Exactly 2 jets : pT > 20 GeV/c and |η| < 2.0
Table 4. Phase space definition for the visible cross section at the particle level.
Thus, the shape of the distribution of ∆rel pT and ∆S is more important than the absolute
normalization in the extraction of the DPS fraction. Therefore, the unfolding and the
systematic studies are carried out for the shapes of the ∆rel pT and ∆S distributions. The
measured distributions are unfolded to the level of stable particles (lifetime cτ > 10 mm)
within the phase space given in table4.
Unfolding is performed with an iterative Bayesian method [56] that properly takes into
account bin-to-bin migrations. A response matrix is created with simulated events pro-duced with the MadGraph 5 + pythia 6 Monte Carlo event generator. The unfolding
is cross-checked by using the singular value decomposition (SVD) [57] approach.
Itera-tive Bayesian and SVD approaches give consistent results within uncertainties. Various systematic effects are considered and are listed below:
• Model dependence: the sensitivity to the model dependence of the simulations used for the unfolding is estimated by comparing the results unfolded with different MCs. The main effect is due to the simulation of MPI, and is estimated by comparing
the detector-level distributions of ∆rel pT and ∆S unfolded by using MadGraph 5
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independent of ∆rel pT and ∆S. This is taken as an estimate of the systematic
uncertainty due to the model dependence of the simulations.
• Background subtraction: the contribution from various backgrounds is estimated with simulated samples that are subtracted from data before applying any corrections. In order to estimate the systematic uncertainties, the cross sections of the background processes are varied within their uncertainties. The shape of the background
distri-bution is affected by the jet energy scale and E/ uncertainties. The total effect of allT
these uncertainties on the final distribution is less than 0.5%.
• Jet Energy Scale (JES): the four momentum of each jet is varied by the JES uncer-tainty. This variation gives a systematic bias of 1–3%.
• Jet Energy Resolution (JER): the JER is different between data and simulation by 3–8% for |η| < 2.0. A variation of the JER by this amount in the simulation affects the distribution by less than 1%.
• Resolution of E/ : the ET / resolution differs in data and simulation [T 55]; this affects
the ∆S distribution by at most 3.7%. The effect on the ∆rel pT distribution is less
than 1%.
• Pileup: in order to take into account the uncertainty in the luminosity
measure-ment [58] and the total inelastic cross section, an uncertainty of 5% is assigned to the
mean value of the pileup distribution. This uncertainty affects the ∆S distribution
by at most 3.7%, whereas the effect on the ∆rel pT distribution is less than 1%.
Table 5 summarizes the systematic uncertainties for the ∆rel pT and ∆S distributions.
The absolute cross section of W + 2-jet events is not important for the extraction of the DPS contribution. However, for completeness the W+ 2-jet production cross section is also corrected to the particle level. The total cross section for the W + 2-jet production
(including the DPS contribution), within the region defined in table 4, is measured to be
53.4 ± 0.1 (stat.) ± 7.6 (syst.) pb. This is consistent with the particle-level prediction by MadGraph 5 + pythia 8, scaled by the ratio of the NNLO and LO cross section for inclusive W production, yielding 55.6 ± 2.8 pb. Various systematic effects, arising from the sources discussed above, are also evaluated for the total W + 2-jet cross section. In addition to these, the cross section has a systematic uncertainty of 2.2% due to the luminosity
measurement [58]. There is a further uncertainty of 1% in the trigger efficiency and 2% in
the muon identification and selection efficiencies [46]. A summary of the various systematic
uncertainties for the W + 2-jet cross section is given in the last column of table 5.
A comparison of various simulations for inclusive W production with the corrected
distributions is shown in figure4. The ∆relpTand ∆S distributions are properly described
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Source ∆relpT ∆S Cross section
Model dependence ≤ 3.2 ≤ 3.9 11 Background normalization ≤ 0.2 ≤ 0.3 1.0 JES ≤ 1.4 ≤ 2.9 7.4 JER ≤ 0.5 ≤ 0.7 1.3 E/ scaleT ≤ 0.5 ≤ 3.7 3.3 Pileup ≤ 0.8 ≤ 3.7 2.3
Muon ID and trigger — — 2.2
Luminosity — — 2.2
Total ≤ 3.7 ≤ 7.2 14
Table 5. Summary of the systematic uncertainties (in %) for different observables. Uncertainties in integrated luminosity, muon identification (ID), and trigger efficiency only affect the W + 2-jet cross section measurement.
The pythia 8 simulation underestimates the measurements by a factor of 1.5–2.0. This discrepancy is due to the fact that pythia 8 generates only 2→1 and 2→2 processes and most of the additional jets are produced during parton showering, and have a softer
pT spectrum than that measured in data. The difference is mainly in the DPS-sensitive
region. Therefore, event generators used to define SPS backgrounds must include a proper implementation of additional hard radiation. If it is not included, the effect of missing hard radiation might be interpreted as a DPS contribution. Without MPI, the LO and NLO predictions from MadGraph 5 + pythia 8 and powheg 2 + pythia 6 are unable
to describe the data shown in figure 4. The importance of including MPI, for both LO
and NLO simulations in the description of W + 2-jet events, is conclusively shown by the comparisons of data with simulations with and without MPI. In the following sections the contribution of hard MPI is extracted.
5 Strategy for the extraction of the DPS fraction
The fraction of W + 2-jet events produced by DPS is extracted by performing a template
fit to the fully corrected distributions of ∆rel p
Tand ∆S using a binned likelihood method.
Here, the strategy to extract the DPS fraction is discussed, including the definition of the signal and background templates and the corresponding systematic uncertainties.
5.1 DPS signal template
In this analysis, DPS events are required to have one W boson with zero jets from the first interaction and two jets from an independent second interaction, where the jets are required
to have pT > 20 GeV/c and |η| < 2.0. In present analysis, the two interactions are assumed
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T p rel ∆ 0 0.2 0.4 0.6 0.8 1 Data / MC 0.6 0.81 1.2 1.4 1.6 1.8 Uncertainty T p rel ∆ 0 0.2 0.4 0.6 0.8 1 Data / MC 0.6 0.81 1.2 1.4 1.6 1.8 Uncertainty T p rel ∆ 0 0.2 0.4 0.6 0.8 1 Data / MC 0.6 0.81 1.2 1.4 1.6 1.8 Uncertainty T p rel ∆ 0 0.2 0.4 0.6 0.8 1 )T p rel ∆ d( evt dN . evt N 1 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 -1 Ldt = 5 fb∫
= 7 TeV s CMS PYTHIA8 MADGRAPH5 + PYTHIA8 MADGRAPH5 + PYTHIA8, no MPI POWHEG2 + PYTHIA6 POWHEG2 + PYTHIA6, no MPI Data S ∆ 0 0.5 1 1.5 2 2.5 3 Data / MC 0.6 0.81 1.2 1.4 1.6 1.82 2.2 Uncertainty S ∆ 0 0.5 1 1.5 2 2.5 3 Data / MC 0.6 0.81 1.2 1.4 1.6 1.82 2.2 Uncertainty S ∆ 0 0.5 1 1.5 2 2.5 3 Data / MC 0.6 0.81 1.2 1.4 1.6 1.82 2.2 Uncertainty S ∆ 0 0.5 1 1.5 2 2.5 3 S) ∆ d( evt dN . evt N 1 -2 10 -1 10 1 -1 Ldt = 5 fb∫
= 7 TeV s CMS PYTHIA8 MADGRAPH5 + PYTHIA8 MADGRAPH5 + PYTHIA8, no MPI POWHEG2 + PYTHIA6 POWHEG2 + PYTHIA6, no MPI DataFigure 4. Fully corrected data distributions, normalized to unity, for the DPS-sensitive observables ∆rel p
T(left) and ∆S (right). The second panel in both plots shows the ratio of data over Mad-Graph 5 + pythia 8 with and without MPI, whereas in the third panel the ratio with powheg 2 + pythia 6 is shown. The ratio of the data and pythia 8 is shown in the fourth panel of both plots. The band represents the total uncertainty of the data (cf. table 5).
pythia 8 and W + 0-jet events. These W + 0-jet events are selected as a subsample of the W + jets inclusive sample simulated with MadGraph 5 + pythia 8. The DPS templates produced with simulated events are in good agreement with the templates obtained by mixing W + 0-jet and dijet events in the data.
5.2 Background template
In order to construct the background template, inclusive W + jets events are used which are produced with MadGraph 5 followed by hadronization and parton showering with pythia 8. The background template cannot be obtained by simply switching off MPI, because only the MPI events that satisfy the DPS signal definition as discussed above need to be excluded. This requires the tagging of the MPI partons and is achieved, in the case of parton showering with pythia 8, by using the status codes 31–39, which are assigned to partons produced from MPI. The background template is constructed by taking inclusive
W + jets events that contain exactly two jets with pT > 20 GeV/c and |η| < 2.0 and by
removing those with two MPI tagged partons with |η| < 2.0. It has been argued [21] that
the templates for signal and background must be disjoint and that an additional cutoff on
the transverse momentum, pcutT , must be applied to the tagged MPI partons. By applying
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[GeV/c] cut T p 0 5 10 15 20 25 DPS fraction [%] 2 3 4 5 6 7 DPS using ∆S observable MC f observable T p rel ∆ using DPS MC fusing simultaneous fit
DPS MC f DPS evt f = 7 TeV s W + jets, → CMS Simulation, pp
Figure 5. The extracted value of the DPS fraction in W + jets events, simulated with MadGraph 5 + pythia 8, using different background templates obtained by varying the transverse momentum cutoff (pcut
T ) for the second hard interaction. The DPS fractions obtained by performing both simultaneous and individual fits to the ∆rel pTand ∆S observables are shown. The DPS fraction, fDPSevt , for the simulated W + jets events is shown by a dashed black line. The error bars/bands represent the statistical uncertainty added in quadrature to the systematic uncertainty of the DPS template (as discussed in section6).
template. The effect of applying such a cutoff is studied by comparing at simulation level the fitted and true DPS fraction in the MadGraph 5 + pythia 8 sample as a function
of pcutT (figure 5). We observe that, for pcutT < 12 GeV/c, the fitted DPS fraction does not
depend on pcutT and therefore we do not apply any cut on pT of the MPI partons. The
true DPS fraction in MadGraph 5 + pythia 8 simulation is obtained by counting events containing a W boson and exactly two jets within the acceptance at particle level. The two MPI-tagged partons must also be within the acceptance (|η| < 2.0) and there must be no
parton with |η| < 2.0 from the first interaction. This fraction, fDPSevt , is determined to be
fDPSevt = 0.046 ± 0.001 (stat.). (5.1)
Because of the different sensitivities of ∆rel pT and ∆S to DPS, the DPS fraction
obtained by fitting only the ∆S observable underestimates fDPSevt , whereas the fitted result
with ∆rel p
T overestimates the fDPSevt . However, the fitted DPS fractions are compatible
with each other within their systematic uncertainties. In these simulation studies, the main source of systematic bias is the model dependence of the signal templates (a detailed
discussion is given in section 6) used for the DPS extraction. If a simultaneous fit of the
∆rel pT and ∆S observables is performed, the fitted fraction is consistent with fDPSevt .
The two observables ∆rel pT and ∆S are not correlated for signal events; conversely
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T p rel ∆ 0 0.2 0.4 0.6 0.8 1 Ratio 0.81 1.2 1.4 1.6 1.8 MC) * Background) DPS * Signal + (1-f MC DPS Inclusive/(f Inclusive/Background T p rel ∆ 0 0.2 0.4 0.6 0.8 1 )T p rel∆ d( evt dN . evt N 1 0.02 0.04 0.06 0.08 0.10.12 Inclusive MADGRAPH5 + PYTHIA8DPS Signal
SPS Background = 7 TeV s W + jets, → CMS Simulation, pp S ∆ 0 0.5 1 1.5 2 2.5 3 Ratio 0.81 1.2 1.4 1.6 1.82 2.2 MC) * Background) DPS * Signal + (1-f MC DPS Inclusive/(f Inclusive/Background ∆S 0 0.5 1 1.5 2 2.5 3 S) ∆ d( evt dN . evt N 1 -3 10 -2 10 -1 10
1 Inclusive MADGRAPH5 + PYTHIA8DPS Signal SPS Background = 7 TeV s W + jets, → CMS Simulation, pp
Figure 6. Fit results for the DPS-sensitive observables ∆rel pT (left) and ∆S (right) using signal and background templates. The distributions of the simulated W + 2-jet events are fitted with signal and background templates. The bottom panels show the ratio of the distributions to the fit results. Here, the term “inclusive” means the simulation also includes the DPS contribution.
to the 2-dimensional distribution of ∆rel pT and ∆S. The result differs by 4% from that
obtained by simultaneously fitting the two one-dimensional distributions. This effect of the correlation on the DPS fraction is small as compared to the total systematic uncertainty
of 26% (as discussed in section6).
Figure 6 shows the results of fitting the ∆rel pT and ∆S observables for simulated W
+ 2-jet events with signal and background templates. The extracted DPS fraction in the simulated events is
fDPSMC = 0.045 ± 0.002 (stat.), (5.2)
which is consistent with the fDPSevt value predicted by the default MPI model present
in the MadGraph 5 + pythia 8 simulation. This closure test also works well
when fitting pseudo-data obtained by mixing simulated signal and background events in different proportions.
To summarize, we perform a simultaneous fit of the ∆rel pT and ∆S observables to
utilize their different sensitivities and to reduce the uncertainties. The signal template is obtained by randomly mixing independently produced W and dijet events, whereas the background template is produced from the W + 2-jet sample simulated with MadGraph 5 + pythia 8, in which events with MPI-tagged partons within the acceptance (|η| < 2.0) are removed.
6 The DPS fraction in data
The ∆rel pT and ∆S distributions are simultaneously fitted by using the signal and
back-ground templates defined in section5. The fitted value of the DPS fraction (fDPS) is:
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T p rel ∆ 0 0.2 0.4 0.6 0.8 1 Ratio 0.8 0.91 1.1 1.2 1.3 1.41.5 Data/(fDPS* Signal + (1 - fDPS) * Background) Data/Background 0 0.2 0.4 0.6 0.8 1 )T p rel∆ d( evt dN . evt N 1 0.02 0.04 0.06 0.08 0.1 0.12 DataDPS Signal SPS Background -1 Ldt = 5 fb
∫
= 7 TeV s CMS S ∆ 0 0.5 1 1.5 2 2.5 3 Ratio 0.81 1.2 1.4 1.6 1.82 2.2 ) * Background) DPS * Signal + (1 - f DPS Data/(f Data/Background 0 0.5 1 1.5 2 2.5 3 S) ∆ d( evt dN . evt N 1 -3 10 -2 10 -1 10 1 Data DPS Signal SPS Background -1 Ldt = 5 fb∫
= 7 TeV s CMSFigure 7. Fit results for the DPS-sensitive observables ∆rel pT (left) and ∆S (right). Corrected data distributions are fitted with signal and background templates (as discussed in section5).
The MC predictions using the fit results are shown in figure 7and compared to data.
The following sources of systematic uncertainties are investigated:
• the signal template is generated by randomly mixing W + 0-jet and dijet events from simulated events. The systematic uncertainty in the signal template is calculated by using different simulations for dijet events, i.e. pythia 8, powheg 2, and
her-wig++ [59]. In this signal definition, the first and second interactions are assumed to
be completely independent of each other. In order to study possible effects of colour reconnection and energy conservation between the first and the second interactions, an additional cross-check is performed by using the pythia 8 event generator for producing W bosons from the first interaction and the dijet from the second
interac-tion. From figure 4it has been concluded that pythia 8 fails to describe W + 2-jet
measurement due to missing contributions from 2→3 and higher order processes. However, the DPS signal definition only includes W + 0-jet from the first interac-tion and exactly two jets from the second interacinterac-tion, which are essentially 2→1 and 2→2 processes, respectively. Therefore, pythia 8 gives a reliable simulation of the DPS signal events. There is a significant contribution from the combinatorial background at particle level, i.e. at least one of the two jets is not from the sec-ond interaction. For systematic studies, this combinatorial background is removed by performing jet-parton matching. The variation in the signal template introduces a systematic uncertainty of 9% in the extracted DPS fraction. MPI effects are in-cluded in both sets of events used for constructing the signal template; the effect of double counting is studied by switching off MPI in both sets of events, and found to be negligible.
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the corresponding systematic uncertainty, the background template is obtained from various simulations and parton showering models, i.e. MadGraph 5 + pythia 8, MadGraph 5 + pythia 6 tune Z2*, and powheg 2 + herwig 6. The MPI partons cannot be tagged in pythia 6 and herwig 6. The background template is therefore obtained by switching off MPI. The systematic uncertainty is evaluated by comparing the DPS fractions using different background templates with MPI off. In this proce-dure, the systematic uncertainty might be overestimated because of the construction of the background templates with simulations in which MPI are turned off. These variations in the background template introduce a systematic uncertainty of 17% in the DPS fraction.
The systematic uncertainty related to the missing higher order diagrams in Mad-Graph 5 is estimated by varying the QCD renormalization and factorization scales simultaneously up and down by a factor of two. This variation gives a systematic effect of 10% on the extracted value of the DPS fraction.
The ME/PS matching scale is 20 GeV for the MadGraph 5 + pythia 8 simulation.
This pT-threshold controls the matching of partons produced from the matrix element
and that from parton showering. Systematic effects related to the matching scale are
estimated by varying the pT-threshold from 15 GeV/c to 25 GeV/c. This variation
gives a systematic uncertainty of 8% in the DPS fraction.
The total systematic uncertainty on the background template is estimated to be 21%. • The effect of the uncertainty in PDFs used for the simulated sample is studied by
using the PDF reweighting method and the prescription given in ref. [60]. The PDF
reweighting only affects the hard scatterings and not the MPI and parton showers. The PDF uncertainties have little effect on the signal template but the variations in the background template result in an uncertainty of 5% in the extracted DPS fraction. • The systematic uncertainty due to the limited number of simulated events for the background template is obtained by varying the template within its statistical un-certainty. This gives a systematic uncertainty of 5% in the extracted DPS fraction. • The corrected measured distributions have various systematic uncertainties, as
dis-cussed in section4. The effect of these systematic biases is evaluated by varying the
shape of the measured distributions within uncertainties. This variation affects the DPS fraction by 10%.
Table 6summarizes the various systematic uncertainties in the extracted value of the
DPS fraction.
7 Determination of the effective cross section
As discussed in section2, the effective cross section can be written as
σeff=
R
fDPS
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Source Uncertainty (%) Signal template 9 Background template 21 PDFs 5 Limited MC statistics 5Uncertainty in corrected data 10
Total 26
Table 6. Systematic uncertainties in the DPS fraction determination.
Source Uncertainty (%) Model dependence 9 JES 7 JER 2 Background 2 Pileup 1 Total 12
Table 7. Systematic uncertainties in the measurement of R.
To calculate the effective cross section, in addition to fDPS, the measurements of the dijet
cross section and R are also necessary. They are discussed below.
7.1 Measurement of R
The ratio, R, of the yield of events with a W boson in the final state and no jets to the
yield of events with a W boson and exactly two jets with pT > 20 GeV/c and |η| < 2.0 is
25.9 ± 0.2 (stat.) at detector level. After subtracting the background contributions from the data, this ratio becomes 27.0 ± 0.2 (stat.). The ratio R is unfolded to particle level with a
correction factor, (Rgen/Rreco), of 1.03 is calculated with MadGraph 5 + pythia 6. The
corrected value of R is 27.8 with a statistical uncertainty of 0.7%. The measurement of R
has a total systematic uncertainty of 12% due to various sources, as listed in table 7. The
measured value of R is:
R = 27.8 ± 0.2 (stat.) ± 3.3 (syst.). (7.2)
7.2 Measurement of σ2j0
The cross section for dijet production with pT> 20 GeV/c and |η| < 2.0 (σ02j) is measured
with pp collision data at a centre-of-mass energy of 7 TeV collected during 2010. A combi-nation of minimum bias and single-jet triggers is used, as for the inclusive jet cross section
measurement [61]. For each trigger, the offline jet pT threshold is chosen such that the
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Source Uncertainty (%) Model dependence 8 JES 13 JER 2 Total 15Table 8. Uncertainties in the measured value of the dijet cross section.
fDPS 0.055 ± 0.002 (stat.) ± 0.014 (syst.)
R 27.8 ± 0.2 (stat.) ± 3.3 (syst.)
σ2j0 0.0409 ± 0.0004 (stat.) ± 0.0061 (syst.) mb
Effective cross section 20.7 ± 0.8 (stat.) ± 6.6 (syst.) mb
Table 9. Measured value of fDPS, R, σ2j0 , and the effective cross section.
At detector level, the cross section for events with exactly two jets with pT> 20 GeV/c
and |η| < 2.0 is measured to be 0.046 mb. This is corrected to particle level with a cor-rection factor of 0.89 calculated from the pythia 6 simulation. The 8% uncertainty in the correction factor is due to the model dependence, estimated by comparing the corrections obtained from the pythia 6 and herwig++ samples. There are further systematic
un-certainties of 13% and 2% due to the JES and JER unun-certainties, respectively. Table 8
summarizes the various sources of systematic uncertainties. The production cross section
σ2j0 at particle level is:
σ2j0 = 0.0409 ± 0.0004 (stat.) ± 0.0061 (syst.) mb. (7.3)
With the values of fDPS, R, and σ02jin eq. (7.1), the effective cross section is determined
to be:
σeff = 20.7 ± 0.8 (stat.) ± 6.6 (syst.) mb. (7.4)
The results of the measurements of R, the DPS fraction, the dijet cross section, and
the effective cross section are summarized in table 9. Figure8 shows a comparison of the
effective cross sections obtained using different processes at various centre-of-mass
ener-gies. In some theoretical models [22], σeff is expressed as a simple geometrical integral
that is independent of the collision energies. Conversely, pythia predicts an increase of
σeff with centre-of-mass energy because of the parameter pT0(
√
s), which depends on the collision energy. From the experimental results, a firm conclusion on the energy
depen-dence of σeff cannot be drawn because of the large systematic uncertainties. The CMS
measurement is consistent with previous measurements performed at the Tevatron and by the ATLAS Collaboration at the LHC. The CMS measurement is also consistent with pre-dictions from pythia of 20–30 mb, depending on the tune. The measured effective cross
section is also consistent with that obtained by fitting the underlying event data [62] with
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[TeV] s 0.04 0.1 0.2 1 2 3 4 5 10 [mb] eff σ 5 10 15 20 25 30 35 40 CMS (W + 2 jets) ATLAS (W + 2 jets) CDF (4 jets) + 3 jets) γ CDF ( + 3 jets) γ Corrected CDF ( + 3 jets) γ D0 (UA2 (4 jets - lower limit) AFS (4 jets - no errors given)
Figure 8. Centre-of-mass energy dependence of σeff measured by different experiments using different processes [8–12, 21]. These measurements used different approaches for extraction of the DPS fraction and σeff. The “Corrected CDF” data point indicates the σeff value corrected for the exclusive event selection [23].
8 Summary
A study of double parton scattering in W + 2-jet events in pp collisions is presented. The
data correspond to an integrated luminosity of 5 fb−1 collected in pp collisions at a
centre-of-mass energy of 7 TeV. Events with a W boson, reconstructed from the muon and E/T
information, are required to have exactly two jets with pT > 20 GeV/c and |η| < 2.0. The
DPS-sensitive observables ∆rel pT and ∆S are corrected for detector effects and selection
efficiencies. Simulations of W + jets events with MadGraph 5 + pythia 8 (or pythia 6) and NLO predictions of powheg 2 + pythia 6 (or herwig 6) provide a good description of the observables and describe the data only if multiple parton interactions are included. The fraction of DPS in W + 2-jet events is extracted with a DPS + SPS template
fit to the distribution of the ∆rel pT and ∆S observables. The obtained value of the DPS
fraction is
fDPS= 0.055 ± 0.002 (stat.) ± 0.014 (syst.), (8.1)
and the effective cross section, characterizing the effective transverse area of hard partonic interactions in collisions between protons, is calculated to be
σeff = 20.7 ± 0.8 (stat.) ± 6.6 (syst.) mb. (8.2)
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AcknowledgmentsWe would like to thank M. Diehl, P. Nason, M.H. Seymour, T. Sj¨ostrand, P. Skands and
D. Treleani for key suggestions related to the theoretical interpretation of the measurement. We congratulate our colleagues in the CERN accelerator departments for the excellent per-formance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and opera-tion of the LHC and the CMS detector provided by the following funding agencies: the Austrian Federal Ministry of Science and Research and the Austrian Science Fund; the Belgian Fonds de la Recherche Scientifique, and Fonds voor Wetenschappelijk Onderzoek; the Brazilian Funding Agencies (CNPq, CAPES, FAPERJ, and FAPESP); the Bulgarian Ministry of Education and Science; CERN; the Chinese Academy of Sciences, Ministry of Science and Technology, and National Natural Science Foundation of China; the Colom-bian Funding Agency (COLCIENCIAS); the Croatian Ministry of Science, Education and Sport, and the Croatian Science Foundation; the Research Promotion Foundation, Cyprus; the Ministry of Education and Research, Recurrent financing contract SF0690030s09 and European Regional Development Fund, Estonia; the Academy of Finland, Finnish Min-istry of Education and Culture, and Helsinki Institute of Physics; the Institut National de
Physique Nucl´eaire et de Physique des Particules / CNRS, and Commissariat `a l’ ´Energie
Atomique et aux ´Energies Alternatives / CEA, France; the Bundesministerium f¨ur Bildung
und Forschung, Deutsche Forschungsgemeinschaft, and Helmholtz-Gemeinschaft Deutscher Forschungszentren, Germany; the General Secretariat for Research and Technology, Greece; the National Scientific Research Foundation, and National Innovation Office, Hungary; the Department of Atomic Energy and the Department of Science and Technology, India; the Institute for Studies in Theoretical Physics and Mathematics, Iran; the Science Foundation, Ireland; the Istituto Nazionale di Fisica Nucleare, Italy; the Korean Ministry of Education, Science and Technology and the World Class University program of NRF, Republic of Ko-rea; the Lithuanian Academy of Sciences; the Mexican Funding Agencies (CINVESTAV, CONACYT, SEP, and UASLP-FAI); the Ministry of Business, Innovation and Employ-ment, New Zealand; the Pakistan Atomic Energy Commission; the Ministry of Science
and Higher Education and the National Science Centre, Poland; the Funda¸c˜ao para a
Ciˆencia e a Tecnologia, Portugal; JINR, Dubna; the Ministry of Education and Science
of the Russian Federation, the Federal Agency of Atomic Energy of the Russian Federa-tion, Russian Academy of Sciences, and the Russian Foundation for Basic Research; the Ministry of Education, Science and Technological Development of Serbia; the Secretar´ıa de
Estado de Investigaci´on, Desarrollo e Innovaci´on and Programa Consolider-Ingenio 2010,
Tech-JHEP03(2014)032
nology Development Agency of Thailand; the Scientific and Technical Research Council of Turkey, and Turkish Atomic Energy Authority; the Science and Technology Facilities Council, U.K.; the US Department of Energy, and the US National Science Foundation. Individuals have received support from the Marie-Curie programme and the European Re-search Council and EPLANET (European Union); the Leventis Foundation; the A. P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy
Office; the Fonds pour la Formation `a la Recherche dans l’Industrie et dans l’Agriculture
(FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the Ministry of Education, Youth and Sports (MEYS) of Czech Republic; the Council of Science and Industrial Research, India; the Compagnia di San Paolo (Torino); the HOMING PLUS programme of Foundation for Polish Science, cofinanced by EU, Re-gional Development Fund; and the Thalis and Aristeia programmes cofinanced by EU-ESF and the Greek NSRF.
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JHEP03(2014)032
The CMS collaborationYerevan Physics Institute, Yerevan, Armenia
S. Chatrchyan, V. Khachatryan, A.M. Sirunyan, A. Tumasyan
Institut f¨ur Hochenergiephysik der OeAW, Wien, Austria
W. Adam, T. Bergauer, M. Dragicevic, J. Er¨o, C. Fabjan1, M. Friedl, R. Fr¨uhwirth1,
V.M. Ghete, C. Hartl, N. H¨ormann, J. Hrubec, M. Jeitler1, W. Kiesenhofer, V. Kn¨unz,
M. Krammer1, I. Kr¨atschmer, D. Liko, I. Mikulec, D. Rabady2, B. Rahbaran, H. Rohringer,
R. Sch¨ofbeck, J. Strauss, A. Taurok, W. Treberer-Treberspurg, W. Waltenberger,
C.-E. Wulz1
National Centre for Particle and High Energy Physics, Minsk, Belarus V. Mossolov, N. Shumeiko, J. Suarez Gonzalez
Universiteit Antwerpen, Antwerpen, Belgium
S. Alderweireldt, M. Bansal, S. Bansal, T. Cornelis, E.A. De Wolf, X. Janssen, A. Knutsson, S. Luyckx, L. Mucibello, S. Ochesanu, B. Roland, R. Rougny, H. Van Haevermaet, P. Van Mechelen, N. Van Remortel, A. Van Spilbeeck
Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, S. Blyweert, J. D’Hondt, N. Heracleous, A. Kalogeropoulos, J. Keaveney, T.J. Kim, S. Lowette, M. Maes, A. Olbrechts, D. Strom, S. Tavernier, W. Van Doninck, P. Van Mulders, G.P. Van Onsem, I. Villella
Universit´e Libre de Bruxelles, Bruxelles, Belgium
C. Caillol, B. Clerbaux, G. De Lentdecker, L. Favart, A.P.R. Gay, A. L´eonard, P.E. Marage,
A. Mohammadi, L. Perni`e, T. Reis, T. Seva, L. Thomas, C. Vander Velde, P. Vanlaer,
J. Wang
Ghent University, Ghent, Belgium
V. Adler, K. Beernaert, L. Benucci, A. Cimmino, S. Costantini, S. Dildick, G. Garcia, B. Klein, J. Lellouch, J. Mccartin, A.A. Ocampo Rios, D. Ryckbosch, S. Salva Diblen, M. Sigamani, N. Strobbe, F. Thyssen, M. Tytgat, S. Walsh, E. Yazgan, N. Zaganidis
Universit´e Catholique de Louvain, Louvain-la-Neuve, Belgium
S. Basegmez, C. Beluffi3, G. Bruno, R. Castello, A. Caudron, L. Ceard, G.G. Da
Silveira, C. Delaere, T. du Pree, D. Favart, L. Forthomme, A. Giammanco4, J. Hollar,
P. Jez, M. Komm, V. Lemaitre, J. Liao, O. Militaru, C. Nuttens, D. Pagano, A. Pin,
K. Piotrzkowski, A. Popov5, L. Quertenmont, M. Selvaggi, M. Vidal Marono, J.M. Vizan
Garcia
Universit´e de Mons, Mons, Belgium
N. Beliy, T. Caebergs, E. Daubie, G.H. Hammad
JHEP03(2014)032
Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
W.L. Ald´a J´unior, W. Carvalho, J. Chinellato6, A. Cust´odio, E.M. Da Costa, D. De Jesus
Damiao, C. De Oliveira Martins, S. Fonseca De Souza, H. Malbouisson, M. Malek, D. Matos Figueiredo, L. Mundim, H. Nogima, W.L. Prado Da Silva, J. Santaolalla, A. Santoro,
A. Sznajder, E.J. Tonelli Manganote6, A. Vilela Pereira
Universidade Estadual Paulistaa, Universidade Federal do ABCb, S˜ao Paulo,
Brazil
C.A. Bernardesb, F.A. Diasa,7, T.R. Fernandez Perez Tomeia, E.M. Gregoresb, C. Laganaa,
P.G. Mercadanteb, S.F. Novaesa, Sandra S. Padulaa
Institute for Nuclear Research and Nuclear Energy, Sofia, Bulgaria
V. Genchev2, P. Iaydjiev2, A. Marinov, S. Piperov, M. Rodozov, G. Sultanov, M. Vutova
University of Sofia, Sofia, Bulgaria
A. Dimitrov, I. Glushkov, R. Hadjiiska, V. Kozhuharov, L. Litov, B. Pavlov, P. Petkov Institute of High Energy Physics, Beijing, China
J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, R. Du, C.H. Jiang, D. Liang, S. Liang,
X. Meng, R. Plestina8, J. Tao, X. Wang, Z. Wang
State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China
C. Asawatangtrakuldee, Y. Ban, Y. Guo, Q. Li, W. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, L. Zhang, W. Zou
Universidad de Los Andes, Bogota, Colombia
C. Avila, C.A. Carrillo Montoya, L.F. Chaparro Sierra, C. Florez, J.P. Gomez, B. Gomez Moreno, J.C. Sanabria
Technical University of Split, Split, Croatia N. Godinovic, D. Lelas, D. Polic, I. Puljak
University of Split, Split, Croatia Z. Antunovic, M. Kovac
Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, K. Kadija, J. Luetic, D. Mekterovic, S. Morovic, L. Tikvica University of Cyprus, Nicosia, Cyprus
A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis Charles University, Prague, Czech Republic
M. Finger, M. Finger Jr.
Academy of Scientific Research and Technology of the Arab Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
A.A. Abdelalim9, Y. Assran10, S. Elgammal9, A. Ellithi Kamel11, M.A. Mahmoud12,
